Probit regression - coefficients in contingent valuation

#1
Dear all,

I have conducted a double-bounded contingent valuation study and analysed it in Stata with Lopez-Feldmann's (2012) doubleb command. The resulting coefficients are confusing me. I am not sure whether they are standardised or unstandardised and this is not helping with my conclusions.

I know that probit regression usually returns standardised coefficients, so I would not be able to say that a 1 unit increase in the IV results in a (coef.) increase in the DV. However, since the Lopez-Feldmann model is a variation of probit regression and his explanation of it shows the coefficients to be unstandardised(see below), I have interpreted my results in this way. But now I am uncertain and need to defend my results on Wednesday.

From Lopez-Feldmann (2012) - https://mpra.ub.uni-muenchen.de/41018/2/MPRA_paper_41018.pdf
The last output before the references shows the difference between male and female WTP to be exactly that of the coefficient : so this would mean the coefficient is standardised, right?

I have now interpreted all my coefficients (some linear, some categorical some nominal) as unstandardised. Is this correct? If so, how can I argue that it is correct that they are unstandardised although they are usually standardised in probit?

Here is my Stata output:


initial: log likelihood = -<inf> (could not be evaluated)
feasible: log likelihood = -6032.3338
rescale: log likelihood = -466.69701
rescale eq: log likelihood = -464.61633
Iteration 0: log likelihood = -464.61633 (not concave)
Iteration 1: log likelihood = -447.67555 (not concave)
Iteration 2: log likelihood = -410.05117 (not concave)
Iteration 3: log likelihood = -402.94301 (not concave)
Iteration 4: log likelihood = -399.09683
Iteration 5: log likelihood = -375.40299
Iteration 6: log likelihood = -364.54334
Iteration 7: log likelihood = -361.66365
Iteration 8: log likelihood = -361.64446
Iteration 9: log likelihood = -361.64445

Number of obs = 137
Wald chi2(8) = 53.00
Log likelihood = -361.64445 Prob > chi2 = 0.0000

------------------------------------------------------------------------------
| Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
Beta |
age | -.3450866 .1649973 -2.09 0.036 -.6684753 -.0216978
gender | 7.255382 3.653988 1.99 0.047 .0936969 14.41707
income | -.0000227 .0020633 -0.01 0.991 -.0040666 .0040212
org | 2.666961 1.273459 2.09 0.036 .1710266 5.162896
qualU | 19.41732 3.6545 5.31 0.000 12.25463 26.58001
self | -.0649317 .1229359 -0.53 0.597 -.3058816 .1760183
env | .2843504 .1289765 2.20 0.027 .0315611 .5371396
soc | .1056503 .1646891 0.64 0.521 -.2171343 .4284349
_cons | 44.36785 11.70759 3.79 0.000 21.4214 67.3143
-------------+----------------------------------------------------------------
Sigma |
_cons | 16.75996 1.485025 11.29 0.000 13.84937 19.67056
------------------------------------------------------------------------------

First-Bid Variable: bid1
Second-Bid Variable: bid2
First-Response Dummy Variable: answer1
Second-Response Dummy Variable: answer2


So as an example: I said that an increase from one category of organic buying frequency to the next (org) causes a 2.67 percentage point increase in Willingness to Pay.

Any help would be very much appreciated

Thanks
Joe